Resilient Distribution System Reconfiguration Based on Genetic Algorithms Considering Load Margin and Contingencies

Jorge Muñoz (), Luis Tipán, Cristian Cuji and Manuel Jaramillo
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Jorge Muñoz: Smart Electric Grids Research Group GIREI (Spanish Acronym), Salesian Polytechnic University, Quito 170702, Ecuador
Luis Tipán: Smart Electric Grids Research Group GIREI (Spanish Acronym), Salesian Polytechnic University, Quito 170702, Ecuador
Cristian Cuji: Smart Electric Grids Research Group GIREI (Spanish Acronym), Salesian Polytechnic University, Quito 170702, Ecuador
Manuel Jaramillo: Smart Electric Grids Research Group GIREI (Spanish Acronym), Salesian Polytechnic University, Quito 170702, Ecuador

Energies, 2025, vol. 18, issue 11, 1-22

Abstract: This paper addresses the challenge of restoring electrical service in distribution systems (DS) under contingency scenarios using a genetic algorithm (GA) implemented in MATLAB. The proposed methodology seeks to maximize restored load, considering operational constraints such as line loadability, voltage limits, and radial topology preservation. It is evaluated with simulations on the IEEE 34-bus test system under four contingency scenarios that consider the disconnection of specific branches. The algorithm’s ability to restore service is demonstrated by identifying optimal auxiliary line reconnections. The method maximizes restored load, achieving between 97% and 99% load reconnection, with an average of 98.8% across the four cases analyzed. Bus voltages remain above 0.95 pu and below the upper limit. Furthermore, test feeder results demonstrate that line loadability is mostly below 60% of the post-reconfiguration loadability.

Keywords: contingency analysis; distribution system reconfiguration; genetic algorithms; load margin; resilient power systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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